Hybrid PSO-Optimized ANFIS-Based Model to Improve Dynamic Voltage Stability

This paper proposes the prediction of hydrate formation pressure in deepwater pipeline with an approach of intelligent optimization. The proposed novel correlation of hydrate formation is using the function of ordinary differential equation. The developed optimization prediction model was founded on the constant coefficients which were examined by a multiple set of experimental data of methane (CH4), ethane (C2H6), propane (C3H8), iso-butane (iC4), nitrogen (N), Carbon Dioxide (CO2) and hydrogen sulfide (H2S) hydrates. The consequences of this research are highly optimistic for the natural gas production industry.

Section: Resultsmentioning

confidence: 99%

Evaluating Pressure in Deepwater Gas Pipeline for the Prediction of Natural Gas Hydrate Formation

Abbasi

Hashim

2019

“…Authors in [9] proposed a new approach to address the optimal design of a FNN using selfadaptive penalty functions. Authors in [10] proposed a Particle Swarm Optimization (PSO)-based neuro-fuzzy model to enhance dynamic voltage stability of a wind connected grid. Authors in [11] proposed a PSO-powered back-propagation neural network load-shedding strategy in the post-fault condition in a microgrid.…”

Section: A Related Workmentioning

confidence: 99%

Levy Enhanced Cross Entropy-based Optimized Training of Feedforward Neural Networks

Pandya

Dabhi

Mochi

et al. 2022

An Artificial Neural Network (ANN) is one of the most powerful tools to predict the behavior of a system with unforeseen data. The feedforward neural network is the simplest, yet most efficient topology that is widely used in computer industries. Training of feedforward ANNs is an integral part of an ANN-based system. Typically an ANN system has inherent non-linearity with multiple parameters like weights and biases that must be optimized simultaneously. To solve such a complex optimization problem, this paper proposes the Levy Enhanced Cross Entropy (LE-CE) method. It is a population-based meta-heuristic method. In each iteration, this method produces a "distribution" of prospective solutions and updates it by updating the parameters of the distribution to obtain the optimal solutions, unlike traditional meta-heuristic methods. As a result, it reduces the chances of getting trapped into local minima, which is the typical drawback of any AI method. To further improve the global exploration capability of the CE method, it is subjected to the Levy flight which consists of a large step length during intermediate iterations. The performance of the LE-CE method is compared with state-of-the-art optimization methods. The result shows the superiority of LE-CE. The statistical ANOVA test confirms that the proposed LE-CE is statistically superior to other algorithms.

“…Voltage regulation, power factor correction, load balancing and harmonic filtering are some of the functions that are supported by STATCOMs. Application of intelligent controllers like to compensate reactive power absorption, improves the dynamic voltage stability of a grid-connected wind power system [17]. The major advantage of a STATCOM distribution as compared to SVC's is its reduced size [18].…”

Section: Introductionmentioning

confidence: 99%

ANN Based DC Link Control of STATCOM in Wind Integrated Distribution System for Power Quality Conditioning

Rani

Prabakaran

2020

The integration of Renewable Energy Sources (RES) brings along abnormalities that affect the grid, loads, and may degrade the performance of the system. These issues can be alleviated with the integration of RES with the use of a distribution Static Synchronous Compensator (STATCOM). Renewable generation with STATCOM provides quality of power during disturbances created by the AC loads and intermittent power from the RES. The STATCOM distribution of DC link plays a major role in the supply quality during abnormalities. In this work, an attempt has been made to provide supply quality in the distribution system with the integration of a renewable energy farm using Artificial Neural Network (ANN)-based DC link STATCOM control of distribution. The wind farm is analyzed for a Double Fed Induction Generator (DFIG) based wind turbine system and it is integrated into the distribution system. The system was simulated in MATLAB 2018A.